Ship Faster. Cut AI Waste. Scale Quality.

Tetriz is the self-improving enterprise operating system for AI-native engineering teams.

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The 5 building blocks of the operating system

Each one makes the next better — so your gains compound instead of resetting every sprint.

Self-improving loop
Agent-steering
Loop EngineeringGraph EngineeringPrompt Engineering
Enterprise Memory

Turbocharged development with tacit organisational knowledge.

Harness

The environment every agent runs in, and what to fix in it.

Observability
AI AdoptionAI Impact
Personal
coachingon every layer

Move up the AI-native curve 2x faster

Compounding every run is what removes the AI Tax — you ship faster, at better quality, for less, and reach AI-native sooner.

Know more about the AI-Native Curve
CompoundingReinventingIntegratingDeployingPiloting← AI-NATIVE VALUE INDEX1·Gated2·Assisted3·Parallel4·Supervised5·AI-nativeAUTONOMY JOURNEY →The gap is the AI TaxWith TetrizWithout Tetriz1/2 the time2× the time

Tetriz reads every tool already in use

Tetriz connects each AI coding session to the pull request it produced — prompt-to-PR attribution — and turns that record into compounding gains in speed, cost, and quality.

Cursor
Cursor
Claude Code
GitHub Copilot
GitHub Copilot
Codex
Codex
Kiro
Kiro
Antigravity
Antigravity
GitHub
GitLab
Bitbucket
Jira
Linear
Linear
Confluence
Confluence
Slack
Google Workspace
Explore all integrations

Field notes from the AI-native shift

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Engineering Intelligence

What are DORA metrics? The engineering metric AI just broke

DORA metrics are four validated measures of software delivery performance. In 2026, 85% of developers regularly use AI coding tools and the four metrics were designed for a world where engineers wrote every line. That assumption no longer holds.

4 min read
Engineering Intelligence

Prompt-to-PR attribution: what it is, why GitHub cannot track it, and how to evaluate it

Prompt-to-PR attribution connects the engineer's AI coding session to the pull request it produced. It links AI input quality, agent-authored code percentage, and review outcome in a single dataset. No engineering intelligence tool instruments at this layer today.

6 min read
Engineering Intelligence

The four categories of enterprise AI and why engineering needs its own

Enterprise AI categories split into four types, and most purchasing conversations conflate all of them. A CTO who cannot tell these apart will spend budget on a tool that delivers seat usage dashboards when what the board actually needs is session-level attribution.

6 min read

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15 minutes to first insight on real data, with no workflow change

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